A Nonnegative Locally Linear KNN model for image recognition

Abstract

In this paper, we investigate the non-zero representation coefficients of Locally Linear KNN (LLKNN) and propose a nonnegative extension of LLKNN (NLLKNN) model for image recognition, where representation coefficients are restricted to be nonnegative to avoid meaningless and unreasonable negative coefficients. A multiplicative iterative algorithm with proof of convergence is proposed to solve the proposed NLLKNN model. Then NLLKNN based classifier (NLLKNNC) and Nonnegative Locally Linear Nearest Mean Classifier (NLLNMC) are proposed. We also investigate the robustness of NLLKNNC and NLLNMC to noises and occlusions. The effectiveness of the proposed methods is evaluated on several image recognition tasks such as scene recognition and face recognition. Extensive experimental results show that the proposed algorithm converges very fast and the proposed methods outperform some representative image recognition methods.